code: def test_torch_conversion():
      stacked = convert_to_torch(
          stack_numpy_dict_list(unstacked_example()),
          in_place=True,
          cast=None,
          device="cpu",
      )
      for k, v in convert_to_torch(
          stacked_example(), in_place=True, cast=None, device="cpu"
      ).items():
          assert torch.all(v == stacked[k])
  
file_path: maze/maze/test/train/utils/test_train_utils.py
imports: import numpy
  import torch
  from maze.perception.perception_utils import convert_to_torch
  from maze.train.utils.train_utils import unstack_numpy_list_dict, stack_numpy_dict_list, stack_torch_dict_list
label_desc: Use image node_id 5 for calling a global function with example usage: test_torch_conversion() without return types